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Deprecated Wiki: Windows Build
Jack Gerrits edited this page Apr 29, 2019
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1 revision
- Visual Studio 2013 AND Visual Studio 2015
- Run the following from Windows Command prompt (cmd.exe) to restore nugets. Just restoring in Visual Studio isn't enough, as the build definition already depends on one of the nugets (ANTLR).
- Don't upgrade the solution to Visual Studio 2015. The C++ is not compatible yet.
cd vowpalwabbit
.nuget\NuGet.exe restore vw.sln
- Azure SDK
- WiX to edit the installer
- To run the tests you need to install Java Runtime
- To avoid space/tab/.. reformatting, install Editor Config VS Plugin
Create required Azure resources using
Create vw_azure.config in your local checkout folder (or any parent of it):
storageConnectionString = <storage account key>
inputEventHubConnectionString = <eventhub connection string>
evalEventHubConnectionString = <eventhub connection string>
Run Visual Studio as Administrator (elevated) as the Azure hosted trainer creates and writes to performance counters.
- Select x64 platform (Configuration Manager \ Active solution platfrom)
- Select x64 as test platform (Test \ Test settings \ Default Processor Architecture)
- Don't trust the Visual Studio 2013 debugger on stack frames in C++/CLI. We observed bogus values. Moving to C++ or C# everything was correct.
- Home
- First Steps
- Input
- Command line arguments
- Model saving and loading
- Controlling VW's output
- Audit
- Algorithm details
- Awesome Vowpal Wabbit
- Learning algorithm
- Learning to Search subsystem
- Loss functions
- What is a learner?
- Docker image
- Model merging
- Evaluation of exploration algorithms
- Reductions
- Contextual Bandit algorithms
- Contextual Bandit Exploration with SquareCB
- Contextual Bandit Zeroth Order Optimization
- Conditional Contextual Bandit
- Slates
- CATS, CATS-pdf for Continuous Actions
- Automl
- Epsilon Decay
- Warm starting contextual bandits
- Efficient Second Order Online Learning
- Latent Dirichlet Allocation
- VW Reductions Workflows
- Interaction Grounded Learning
- CB with Large Action Spaces
- CB with Graph Feedback
- FreeGrad
- Marginal
- Active Learning
- Eigen Memory Trees (EMT)
- Element-wise interaction
- Bindings
-
Examples
- Logged Contextual Bandit example
- One Against All (oaa) multi class example
- Weighted All Pairs (wap) multi class example
- Cost Sensitive One Against All (csoaa) multi class example
- Multiclass classification
- Error Correcting Tournament (ect) multi class example
- Malicious URL example
- Daemon example
- Matrix factorization example
- Rcv1 example
- Truncated gradient descent example
- Scripts
- Implement your own joint prediction model
- Predicting probabilities
- murmur2 vs murmur3
- Weight vector
- Matching Label and Prediction Types Between Reductions
- Zhen's Presentation Slides on enhancements to vw
- EZExample Archive
- Design Documents
- Contribute: